Update app.py

app.py

@@ -1,105 +1,72 @@
-from sqlalchemy import create_engine, Table, Column, String, Integer, Float, Text, TIMESTAMP, MetaData
-from sqlalchemy.dialects.postgresql import UUID
-from sqlalchemy import text
-from llama_index.core import SQLDatabase
-from llama_index.core.query_engine import NLSQLTableQueryEngine
-from llama_index.llms.huggingface import HuggingFaceLLM
-import logging
+import gradio as gr
+from huggingface_hub import InferenceClient
 
-# Set up logging
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# PostgreSQL DB connection (converted from JDBC)
-engine = create_engine("postgresql+psycopg2://postgres:password@localhost:5434/postgres")
+demo = gr.ChatInterface(..., title="Wiser AI Assistant")
 
-metadata_obj = MetaData()
 
-# Define the machine_current_log table
-machine_current_log_table = Table(
-    "machine_current_log",
-    metadata_obj,
-    Column("mac", Text, primary_key=True),
-    Column("created_at", TIMESTAMP(timezone=True), primary_key=True),
-    Column("CT1", Float),
-    Column("CT2", Float),
-    Column("CT3", Float),
-    Column("CT_Avg", Float),
-    Column("total_current", Float),
-    Column("state", Text),
-    Column("state_duration", Integer),
-    Column("fault_status", Text),
-    Column("fw_version", Text),
-    Column("machineId", UUID),
-    Column("hi", Text),
-)
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
 
-# Create the table
-metadata_obj.create_all(engine)
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
 
-# Convert to TimescaleDB hypertable
-with engine.connect() as conn:
-    conn.execute(text("SELECT create_hypertable('machine_current_log', 'created_at', if_not_exists => TRUE);"))
-    print("TimescaleDB hypertable created")
-    conn.commit()
+    messages.append({"role": "user", "content": message})
 
-# Query 1: Get all MAC addresses
-print("\nQuerying all MAC addresses:")
-with engine.connect() as con:
-    rows = con.execute(text("SELECT mac from machine_current_log"))
-    for row in rows:
-        print(row)
+    response = ""
 
-# Query 2: Get all data and count
-print("\nQuerying all data and count:")
-stmt = text("""
-    SELECT mac, created_at, CT1, CT2, CT3, CT_Avg, 
-           total_current, state, state_duration, fault_status, 
-           fw_version, machineId
-    FROM machine_current_log
-""")
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
 
-with engine.connect() as connection:
-    print("hello")
-    count_stmt = text("SELECT COUNT(*) FROM machine_current_log")
-    count = connection.execute(count_stmt).scalar()
-    print(f"Total number of rows in table: {count}")
-    results = connection.execute(stmt).fetchall()
-    print(results)
+        response += token
+        yield response
 
-# Set up LlamaIndex natural language querying
-sql_database = SQLDatabase(engine)
 
-llm = HuggingFaceLLM(
-    model_name="HuggingFaceH4/zephyr-7b-beta",
-    context_window=2048,
-    max_new_tokens=256,
-    generate_kwargs={"temperature": 0.7, "top_p": 0.95},
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    title="🤖 Wiser AI Assistant",
+    description="Your smart manufacturing assistant powered by Wiser Machines. Ask me anything about automation, productivity, factory operations, or how Wiser can help!",
+    additional_inputs=[
+        gr.Textbox(value="You are Wiser, an AI assistant specializing in smart manufacturing and factory automation. Respond clearly, concisely, and use real-world manufacturing examples when needed.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
 )
 
-query_engine = NLSQLTableQueryEngine(
-    sql_database=sql_database,
-    tables=["machine_current_log"],
-    llm=llm
-)
 
-def natural_language_query(question: str):
-    try:
-        response = query_engine.query(question)
-        return str(response)
-    except Exception as e:
-        logger.error(f"Query error: {e}")
-        return f"Error processing query: {str(e)}"
+demo = gr.ChatInterface(..., title="Wiser AI Assistant")
 
 if __name__ == "__main__":
-    # Natural language query examples
-    print("\nNatural Language Query Examples:")
-    questions = [
-        "What is the average CT1 reading?",
-        "Which machine has the highest total current?",
-        "Show me the latest fault status for each machine"
-    ]
-    
-    for question in questions:
-        print(f"\nQuestion: {question}")
-        print("Answer:", natural_language_query(question))
+    with gr.Blocks() as demo:
+    gr.Markdown("## Welcome to Wiser AI Assistant")
+    gr.Markdown("Ask questions about factory automation, productivity, or how Wiser Machines can help streamline your operations.")
+    chat = gr.ChatInterface(
+        respond,
+        additional_inputs=[...],  # your sliders and system message
+    )
+
+    demo.launch()